CN112085399B - Method, device and equipment for determining reliability of energy system - Google Patents
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Abstract
The application provides an energy system reliability determining method, device and equipment, wherein the method comprises the following steps: acquiring supply and demand data of a target energy system; generating a first dynamic event tree of the target energy system, wherein the first dynamic event tree comprises all event sequences possibly occurring in the target energy system; according to the supply and demand data and the first dynamic event tree, determining failure probability data corresponding to a plurality of system composition conditions respectively within preset time, wherein the plurality of system composition conditions comprise: the target energy system comprises electric conversion gas and does not comprise electric conversion gas; and determining the reliability of the target energy system under the condition of each system component according to the failure probability data. In the embodiment of the application, the physical model of the target energy system and the uncertainty event possibly occurring in the system operation process can be accurately embodied by using the first dynamic event tree, so that the reliability of the comprehensive energy system containing electricity to gas can be efficiently and accurately determined.
Description
Technical Field
The present disclosure relates to the field of renewable energy technologies, and in particular, to a method, an apparatus, and a device for determining reliability of an energy system.
Background
With the acceleration of the low carbonization process and the development of renewable energy utilization technologies, natural gas has received increasing attention as clean energy and renewable energy in the world energy field. In the development of energy conversion technology, the concept of a comprehensive energy system is provided. The comprehensive energy system refers to an energy production and supply marketing integrated system which is formed by organically coordinating production, transmission, distribution, conversion, storage, consumption and other links of different energy sources (power grid, heat supply network, natural gas pipe network and the like) in the processes of planning, construction, operation and the like, and fully utilizes renewable energy sources to improve the safety and reliability of the system. However, the change and transformation of the energy structure can have great influence on the reliable operation of the energy system, and the safety operation of the comprehensive energy system is ensured to be the basic requirement of daily operation.
The current research on integrated energy systems mainly focuses on the problem of integrated energy system optimization, but the reliability analysis is less. The reliability analysis in the prior art mainly aims at systems such as an electric Power system, a natural Gas pipeline network, a thermodynamic system and the like, but does not aim at a reliability analysis scheme of a comprehensive energy system comprising P2G (Power to Gas), namely the reliability analysis scheme in the prior art has certain limitations. Since P2G is a very important component in integrated energy systems, reliability analysis schemes in the prior art cannot accurately and efficiently determine the reliability of integrated energy systems including P2G.
In view of the above problems, no effective solution has been proposed at present.
Disclosure of Invention
The embodiment of the application provides a method, a device and equipment for determining the reliability of an energy system, which are used for solving the problem that the reliability of a comprehensive energy system containing P2G cannot be accurately and efficiently determined in the prior art.
The embodiment of the application provides an energy system reliability determining method, which comprises the following steps: acquiring supply and demand data of a target energy system; generating a first dynamic event tree of the target energy system, wherein the first dynamic event tree comprises an event sequence which possibly occurs in the target energy system; determining failure probability data corresponding to the target energy system under the condition of a plurality of system components in preset time according to the supply and demand data and the first dynamic event tree, wherein the system components comprise: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas; and determining the reliability of the target energy system under the condition of each system component according to the failure probability data.
In one embodiment, the failure probability data includes: the total failure probability of the target energy system in the preset time and the failure probability in each preset time period in the preset time.
In one embodiment, the plurality of system composition conditions includes: the target energy system comprises liquefied natural gas but does not comprise electric conversion gas, the target energy system comprises electric conversion gas and liquefied natural gas, the target energy system does not comprise electric conversion gas and liquefied natural gas, and the target energy system comprises electric conversion gas but does not comprise liquefied natural gas.
In one embodiment, in the case that the target energy system includes electric power conversion, after determining reliability of the target energy system in each system composition case according to the failure probability data, the method further includes: acquiring target pressure of the target energy system; generating a second dynamic event tree of the target energy system by taking the target pressure as a process variable of the dynamic event tree; calculating a safety margin of the target energy system based on the second dynamic event tree; and determining the reliability of the energy system containing the electric conversion gas according to the safety margin.
In one embodiment, calculating a safety margin for the target energy system based on the second dynamic event tree comprises: determining a plurality of event sequences contained in the second dynamic event tree; simulating based on the second dynamic event tree to obtain pressure change data corresponding to each event sequence in the plurality of event sequences; taking the pressure change data closest to the target pressure in the pressure change data corresponding to each event sequence as target pressure change data; taking the event sequence corresponding to the target pressure change data as a target event sequence; and calculating the safety margin of the target energy system under the target event sequence by utilizing Monte Carlo simulation sampling.
In one embodiment, calculating a safety margin of the target energy system under the target event sequence using Monte Carlo simulation sampling includes: determining a node with the lowest pressure in the target energy system; taking the pressure at the node with the lowest pressure as a reference pressure; and calculating the safety margin of the target energy system under the target event sequence according to the reference pressure by utilizing Monte Carlo simulation sampling.
In one embodiment, the target event sequence includes: the air supply pressure is reduced.
In one embodiment, calculating a safety margin of the target energy system under the target event sequence using Monte Carlo simulation sampling includes: calculating corresponding variances of the reduction amplitudes of different gas source pressures; respectively carrying out Monte Carlo simulation sampling under the condition of different variances to obtain sampling results under the condition of different variances; and calculating the safety margin of the target energy system under the target event sequence under the condition of different variances according to the sampling result under the condition of the different variances.
The embodiment of the application also provides an energy system reliability determining device, which comprises: the acquisition module is used for acquiring supply and demand data of the target energy system; the generation module is used for generating a first dynamic event tree of the target energy system, wherein the first dynamic event tree comprises an event sequence which possibly occurs in the target energy system; the first determining module is configured to determine failure probability data corresponding to each of the target energy systems under a plurality of system composition conditions within a preset time according to the supply and demand data and the first dynamic event tree, where the plurality of system composition conditions include: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas; and the second determining module is used for determining the reliability of the target energy system under the condition of each system composition according to the failure probability data.
The embodiment of the application also provides an energy system reliability determining device, which comprises a processor and a memory for storing instructions executable by the processor, wherein the steps of the energy system reliability determining method are realized when the processor executes the instructions.
The embodiment of the application provides a method for determining the reliability of an energy system, which can generate a first dynamic event tree of the target energy system by acquiring supply and demand data of the target energy system, wherein the first dynamic event tree comprises all event sequences possibly occurring in the target energy system, so that a physical model of the target energy system and uncertainty events possibly occurring in the system operation process can be accurately and comprehensively reflected by using the first dynamic event tree. Further, according to the supply and demand data and the first dynamic event tree, failure probability data corresponding to each of the target energy systems under a plurality of system composition conditions within a preset time may be determined, where the plurality of system composition conditions include: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas, so that the influence of the electric conversion gas on the target energy system can be taken into consideration. The reliability of the target energy system under the condition of each system composition can be determined according to the failure probability data, so that the reliability of the comprehensive energy system containing electric conversion gas can be determined efficiently and accurately.
Drawings
The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this application. In the drawings:
FIG. 1 is a schematic diagram of steps of a method for determining reliability of an energy system according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an abstract structural view of an integrated energy system provided in accordance with embodiments of the present application;
FIG. 3 is a schematic diagram of an evolution process of a dynamic event tree provided in accordance with a specific embodiment of the present application;
FIG. 4 is a schematic diagram of a first dynamic event tree provided in accordance with an embodiment of the present application;
FIG. 5 is a schematic diagram of a variation of the probability of system failure provided in accordance with an embodiment of the present application;
FIG. 6 is a schematic diagram of a second dynamic event tree structure provided in accordance with an embodiment of the present application;
FIG. 7 is a schematic diagram of pressure change data at different event sequences provided in accordance with an embodiment of the present application;
FIG. 8 is a schematic diagram of the structure of an energy system reliability determination device provided according to an embodiment of the present application;
fig. 9 is a schematic structural view of an energy system reliability determination apparatus provided according to an embodiment of the present application.
Detailed Description
The principles and spirit of the present application will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are presented merely to enable one skilled in the art to better understand and practice the present application and are not intended to limit the scope of the present application in any way. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Those skilled in the art will appreciate that embodiments of the present application may be implemented as a system, apparatus device, method, or computer program product. Accordingly, the present disclosure may be embodied in the following forms, namely: complete hardware, complete software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
While the flow described below includes a number of operations occurring in a particular order, it should be apparent that these processes may include more or fewer operations, which may be performed sequentially or in parallel (e.g., using a parallel processor or a multi-threaded environment).
Since the current research on integrated energy systems is mainly focused on the problem of integrated energy system optimization, there is less analysis on reliability. In the prior art, reliability analysis of systems such as a power system, a natural gas pipeline network, a thermodynamic system and the like has been intensively studied, but a mature technical scheme for reliability evaluation of a comprehensive energy system containing P2G is still not available. Reliability assessment of integrated energy systems can be roughly divided into three aspects, firstly interaction between different systems, secondly renewable energy with uncertainty and user requirements, and finally characteristics of different energy systems. Although some research is currently being conducted on the above-mentioned different aspects, power to gas (P2G) modules in integrated energy systems are not considered, and Power to gas is an important component of future energy systems. Therefore, the reliability of the integrated energy system including P2G cannot be accurately and efficiently determined using the reliability analysis schemes in the prior art.
Based on the above problems, the embodiment of the present invention provides a method for determining the reliability of an energy system, as shown in fig. 1, which may include the following steps:
s101: and acquiring supply and demand data of the target energy system.
The target energy system may be an integrated energy system requiring reliability analysis, and the target energy system may include a plurality of power supply nodes and natural gas supply nodes, for supplying gas and power to users at the corresponding nodes. The supply and demand data of the target energy system may be used to characterize the gas-electricity consumer demand, the gas supply amount, and the power plant power generation, and may include, but is not limited to, at least one of the following: power demand, natural gas demand and wind farm power output.
In some embodiments, due to different characteristics at different nodes, supply and demand data at each node in the target energy system may be obtained respectively, correspondingly, reliability at each node is determined respectively, and the reliability of the target energy system is determined according to the reliability at each node, which manner is specifically determined according to the actual situation, which is not limited in this application.
S102: a first dynamic event tree of the target energy system is generated, wherein the first dynamic event tree comprises all event sequences which possibly occur in the target energy system.
In order to uniformly consider a complex physical model of the target energy system and uncertainty events that may occur during system operation, a dynamic event tree (DET, dynamic Event Tree) may be employed to provide a method framework to simulate the evolution of the physical system, thereby enabling interactions between random events and continuous-time behavior in the target energy system to be considered. A first dynamic event tree of the target energy system may be generated according to a physical model of the target energy system, wherein the first dynamic event tree includes all event sequences that may occur in the target energy system.
Specifically, the first dynamic event tree may be generated by considering a complex physical model and uncertainty factors of the target energy system during the simulation process, and evolving the end state of each event sequence based on the physical model through the representation of the process variables and the injection of different events. The dynamic event tree is a relatively comprehensive simulation method based on an accident scene, and possible consequences can be deduced from initial events according to the time sequence of accident development, so that a dangerous source identification method is performed, and the dynamic event tree method represents the logical relationship between a certain accident possibly happening in a system and various reasons causing the accident by using a tree diagram called an event tree.
The above-mentioned event sequence may be events that may occur at respective time nodes within a preset time, for example: in the case that the preset time is 1 day, a certain event sequence in the first dynamic tree may be: 8:00am compressor failure-12: 00am air source pressure is unchanged-15:00 pm air source pressure is unchanged-19:00 pm user demand increases. The possible event sequence can be obtained by analysis and inference by a professional according to the historical data of the system, or a possible event sequence library is constructed, and the possible event sequence is selected from the event sequence library based on the characteristics of each time node.
S103: according to supply and demand data and a first dynamic event tree, determining failure probability data respectively corresponding to a target energy system under the condition of multiple system composition in a preset time, wherein the multiple system composition conditions comprise: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas.
In some embodiments, the reliability of the target energy system may be evaluated by determining whether the target energy system is capable of meeting the needs of the user. Therefore, according to the supply and demand data and the first dynamic event tree, failure probability data corresponding to the target energy system under the condition that a plurality of systems are formed in a preset time can be determined, and when the supply and demand of the target energy system are unbalanced, the target energy system is considered to be failed.
Wherein, the above-mentioned multiple system composition condition can include: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas. Since the gas supply node may supply gas using P2G or LNG (liquefied natural gas), in some embodiments, the target energy system may further include: the target energy system comprises liquefied natural gas but does not comprise electric conversion gas, the target energy system comprises electric conversion gas and liquefied natural gas, the target energy system does not comprise electric conversion gas and liquefied natural gas, and the target energy system comprises electric conversion gas but does not comprise liquefied natural gas. The above-mentioned electric conversion is a technique for converting electric energy into gaseous fuel, which is a technique for solving the difficult problem of the electric power storage of renewable energy, and three methods are currently adopted, but all methods are to electrolyze water into hydrogen and oxygen by means of electric power.
The failure probability data may be used to characterize the likelihood of failure of the target energy system during use, and may include, but is not limited to, at least one of: the total failure probability of the target energy system in the preset time and the failure probability in each preset time period in the preset time. The preset time may be 1 day, 2 days, 20 hours, etc., and specifically may be determined according to practical situations, which is not limited in this application.
In some embodiments, the preset time may be divided into a plurality of time periods, that is, the plurality of preset time periods, for example, in the case that the preset time is 1, the preset time is divided into four time periods, and then the corresponding plurality of preset time periods are respectively: 0:00-4:00, 4:00-10:00, 10:00-16:00, 16:00-24:00. Of course, other segmentation methods may be adopted, and the specific method can be determined according to practical situations, which is not limited in this application.
In some embodiments, the failure probability data may be determined in real time according to the supply and demand data and the first dynamic event tree within a preset time, so as to accurately evaluate the reliability of the target energy system. The method can also predict the supply and demand data and the first dynamic event tree in a period of time before the preset time to obtain failure probability data, so that the reliability of the target energy system can be evaluated according to the failure probability data obtained by prediction, and the target energy system is correspondingly improved to improve the reliability of the target energy system. The specific mode may be determined according to the actual situation, which is not limited in this application.
In one embodiment, the first dynamic event tree may be analyzed by combining the supply and demand data described above, and the analysis of the first dynamic event tree may be performed on the assumption that: (1) Discretizing a random variable of a target energy system, such as supply and demand at each time point; (2) The target energy system air source supplies air, and the LNG supply and the standby power supply have certain probability of reduction or failure; (3) When the air supply of the air source is reduced and the demand cannot be met, the air supply is firstly carried out through the LNG, and when the LNG still cannot meet the demand, the standby power supply is used for supplying energy; (4) When the supply and demand of the target energy system are unbalanced, the system is considered to be invalid.
S104: and determining the reliability of the target energy system under the condition of each system composition according to the failure probability data.
In some embodiments, the reliability of the target energy system under the condition of each system component can be determined according to the failure probability data, and the lower the failure probability is, the higher the reliability is. From the reliability data that is ultimately determined, it can be found that the target energy system that contains electric power is more reliable than the target energy system that does not contain electric power.
In order to further determine the reliability of the target energy system containing the electric conversion gas, the characteristics of the natural gas system can be considered, namely, the target pressure of the target energy system can be acquired, and the target pressure is used as a process variable of the dynamic event tree to generate a second dynamic event tree of the target energy system. Further, a Safety Margin (SM) of the target energy system may be calculated based on the second dynamic event tree, and the reliability of the energy system including the electric power conversion may be determined according to the safety margin.
In some embodiments, the system is considered unreliable when the safety margin is less than 0, or is considered unreliable when the safety margin is less than a certain preset threshold, and the preset threshold may be 0.1, 0.91, etc., which may be specifically determined according to the actual situation, and is not limited in this application.
The target pressure may be the lowest value of the gas supply pressure required to be provided to the first party (user) by the second party (gas supply party) in the pipeline natural gas supply protocol, and the safety margin may be used to characterize the safety, reliability and system risk of the target energy system.
Because the occurrence scenes and the possible occurrence situations corresponding to different event sequences have certain differences, in one embodiment, when the safety margin of the target energy system is calculated based on the second dynamic event tree, a plurality of event sequences contained in the second dynamic event tree can be determined first, and based on the second dynamic event tree, simulation is performed to obtain pressure change data corresponding to each event sequence in the plurality of event sequences. Further, the pressure change data closest to the target pressure in the pressure change data corresponding to each event sequence may be taken as target pressure change data, and the event sequence corresponding to the target pressure change data may be taken as a target event sequence. The safety margin of the target energy system under the target event sequence can be calculated using Monte Carlo analog sampling.
The pressure change data corresponding to the event sequence may include: the pressure corresponding to each time point in the sequence of events. Since the pressure change data closest to the target pressure can better embody the concept that the target energy system may fail, the pressure change data closest to the target pressure can be used as the target pressure change data, that is, if the target energy system is safe and reliable under the event sequence corresponding to the target pressure change data, the target energy system should be safe and reliable under other event sequences.
The Monte Carlo analog sampling is also called random sampling or statistical test method, belongs to a branch of computational mathematics, and when the problem to be solved is the probability of occurrence of a certain event or the expected value of a certain random variable, the occurrence frequency of the event or the average value of the random variable can be obtained by a certain test method, and the average value of the event or the average value of the random variable is used as the solution of the problem.
In one embodiment, when determining the plurality of event sequences included in the second dynamic event tree, a failed event sequence in the plurality of event sequences may be removed, where the failed event sequence is an event sequence corresponding to a pressure less than the target pressure, that is, a supply-demand imbalance condition.
In one embodiment, data at the lowest pressure node in the target energy system may be analyzed to determine the reliability of the target energy system. I.e. a plurality of event sequences contained at the lowest pressure node in the second dynamic event tree can be determined and the safety margin calculated in the above-described manner based on the plurality of event sequences contained at the lowest pressure node. The node with the lowest pressure may be the node located at the end of the transmission pipeline of the target energy system, or may be determined according to pressure data measured in real time, and the node with the lowest pressure may be determined according to actual situations, which is not limited in the application.
Correspondingly, when the safety margin is calculated, the node with the lowest pressure in the target energy system can be determined first, and the pressure at the node with the lowest pressure is taken as the reference pressure. Further, monte Carlo simulation sampling may be utilized to calculate a safety margin of the target energy system under the target event sequence based on the reference pressure.
Under the condition that the target event sequence comprises the air source pressure reduction, the influence of the air source pressure reduction amplitude on other indexes in the target energy system can be explored, so that the influence of the air source pressure reduction amplitude on the safety of the target energy system in different fluctuation ranges is determined, and the safety and reliability of the system are ensured. In some embodiments, corresponding variances of different gas source pressure reduction amplitudes can be calculated, monte Carlo simulation sampling can be performed under the conditions of different variances, and sampling results under the conditions of different variances can be obtained. According to sampling results under the condition of different variances, the safety margin of the target energy system under the target event sequence under the condition of different variances can be calculated, namely the safety margin of the target energy system under the condition of normal distribution of the gas source pressure change with different variances. Wherein the variance may be used to characterize the fluctuation range of the source pressure decrease amplitude.
It can be understood that the above steps are only illustrative of how to determine the influence of the air source pressure reduction amplitude on the safety of the target energy system in different fluctuation ranges, and the fir method can be used to continuously explore the influence of other factors on other indexes of the target energy system, so as to determine the influence of different influencing factors on the safety of the target energy system in different fluctuation ranges, and ensure the safety and reliability of the target energy system. Specifically, the method can be determined according to actual conditions, and the application is not limited to this.
From the above description, it can be seen that the following technical effects are achieved in the embodiments of the present application: the first dynamic event tree of the target energy system can be generated by acquiring supply and demand data of the target energy system, wherein the first dynamic event tree comprises all event sequences possibly occurring in the target energy system, so that a physical model of the target energy system and uncertainty events possibly occurring in the system operation process can be accurately and comprehensively reflected by using the first dynamic event tree. Further, according to the supply and demand data and the first dynamic event tree, failure probability data corresponding to each of the target energy systems under a plurality of system composition conditions within a preset time may be determined, where the plurality of system composition conditions include: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas, so that the influence of the electric conversion gas on the target energy system can be taken into consideration. The reliability of the target energy system under the condition of each system composition can be determined according to the failure probability data, so that the reliability of the comprehensive energy system containing electric conversion gas can be determined efficiently and accurately.
The above method is described below in connection with a specific embodiment, however, it should be noted that this specific embodiment is only for better illustrating the present application and is not meant to be a undue limitation on the present application.
The implementation of the invention provides a method for determining the reliability of an energy system, which can comprise the following steps:
step 1: electric power and natural gas demand and wind farm electric power output are obtained.
An abstract structural diagram of an integrated energy system may be as shown in fig. 2, which is made up of a natural gas system and an electrical power system (containing renewable energy sources). The natural gas system consists of a pipeline, a gas compression station, a pipeline gas source and an LNG station. The power system is composed of a gas power station, a renewable energy power station, a transformer and a high-voltage line. The two systems are coupled by a gas power station and an electric gas conversion station. node1 is the upstream Gas source input point, and the normal Gas supply capacity is 52000m 3 And/h, node4 is connected with an LNG station, and the normal air supply capacity is 8000m 3 And/h. And the bus3 is connected with a thermal power plant, and the output power of the thermal power plant is 260MW. Bus2 is connected to a wind power plant, the output power of which is related to the actual wind speed. Coal-fired power plant is a renewable energy power station; wind farm is a Wind station; gas-fired power plant Gas power station; power-to-gas is an electric substation; the Customer represents the user.
Other nodes are customer demand points, for example 24 hours a day, with different nodes for power and natural gas demand and wind farm power output as shown in table 1. Wherein p.u is a standardized unit, the power is MW/100, and the natural gas is m 3 /h/10000。
Step 2: a first dynamic event tree of the integrated energy system is generated.
The analysis and generation process of the dynamic event tree is as follows: (1) Defining a physical model and a process variable of the system, and representing the change of the state of the physical model by analyzing the process variable; (2) Defining a branching rule of the system, and determining rules of branching start and end in the evolution process of the system; (3) And generating a dynamic event tree according to the physical model to describe all possible accident scenes. The method is characterized in that the method takes an initial state as an analysis starting point, and evolves forward according to branch rules, absorption rules and the like, and various variables of the system are identified and judged in each step length, so that all event sequences are generated until all possible accident scenes of the system are identified.
TABLE 1 Power and Natural gas demand and wind farm Power output
The evolution process of the dynamic event tree may be represented in a discrete manner for each branch, as shown in fig. 3, and represents the occurrence and evolution of different time scenarios. The complex physical model and uncertainty factors of the system are considered in the simulation process, and the final state of each event sequence is evolved on the basis of the physical model through the representation of the process variables and the injection of different events. The evolution of the dynamic event tree starts from a preset initial state, wherein after determining whether the node satisfies the branching rule, the node stores information of the system state. The first sequence of events of the evolution process is characterized in fig. 3 (a), which evolves to a final state based on the state of the process variable, and the determination of the different states is made by a specific physical model operation. In this simulation, three nodes 1, 2 and 3 that have not evolved completely are generated, and each node records state information of the current system, including system parameters, hardware states, planning states, and so on. After the evolution of the sequence is finished, the evolution of the dynamic event tree returns to the node 3 again, the system state information stored by the node 3 is read, whether other branches are possible or not is judged, the evolution continues to the final state along the new branch, and when the node 3 does not have other branches possible, the node 3 is marked as completely evolved, so that fig. 3 (b) can be obtained. By analogy, the evolution process of the dynamic event tree is returned to the adjacent node which is not completely evolved, and then returned to the node 2, the system information of the node is read, and then the evolution is performed, as shown in fig. 3 (c). Finally, when all nodes and sequences evolve, a complete dynamic event tree is generated, as shown in fig. 3 (d), and the dynamic event tree contains all possible events and scenes.
The first dynamic event tree of the integrated energy system finally generated in the above manner may be shown in fig. 4, where the initiating stat is in a startup state, the diamond is a natural gas supply, the square is LNG, the circle is a storage power (standby power), and the triangle is a failure. Wherein the first dynamic event tree has time as an abscissa.
Step 3: failure probability data is determined by analyzing the first dynamic event tree.
Based on the physical model of the comprehensive energy, the analysis of the dynamic event tree is performed on the basis of the following assumptions: (1) Discretizing a random variable of the system, such as supply and demand at each time point; (2) The system air source supplies air, and the LNG supply air and the standby power supply have certain probability of reduction or failure; (3) When the air supply of the air source is reduced and the demand cannot be met, the air supply is firstly carried out through the LNG, and when the LNG still cannot meet the demand, the standby power supply is used for supplying energy; (4) when the system supply and demand are unbalanced, the system is considered to be invalid.
By analyzing the first dynamic event tree and the data in table 1, the total failure probability of the system during the day and the failure probability of each time period (1 h) can be derived. In fig. 5, the system failure probability is changed under 4 different system composition cases (including lng but not including electric transfer gas, including electric transfer gas and lng, including no electric transfer gas and lng, including electric transfer gas and lng), where the solid line represents the cumulative failure probability and the dotted line represents the failure probability in each time period (1 h). Wherein P is c,i Represents the failure probability of each time period (1 h), P h,i Represents the cumulative failure probability of the system during the day, wherein i is {1,2,3,4}, and i is equal to 1,2,3,4, respectively, represents that the integrated energy system does not contain electric transfer gas and does not contain liquefied natural gas, contains liquefied natural gas but does not contain electric transfer gas, contains electric transfer gas but does not contain liquefied natural gas, contains electric transfer gas and contains liquefied natural gas.
Through the analysis, a decision maker can predict the next-day failure condition in advance, and the possibility of system failure is reduced through some optimization and adjustment, so that the reliability of the system is improved.
Step 4: a second dynamic event tree is generated.
To take into account the nature of the natural gas system, the contractual pressures of the natural gas system are taken into account as the process variables of the dynamic event tree, a second dynamic event tree is generated in the manner of step 2, the second dynamic event tree being generated as schematically illustrated in fig. 6. Wherein diamonds at each node in the second dynamic event tree represent compressor failure, squares represent reduced source pressure, circles represent increased demand, triangles represent system failure, and pentagrams represent system safety.
The basic data used to build the second dynamic event tree is the same as the first dynamic event tree, and the second dynamic event tree is generated on the assumption that: (1) The pressure ratio of the air compressor station is reduced with a certain probability, and the air source pressure is also reduced with a certain probability (obeying normal distribution); (2) The natural gas flow supply is in need of the process; (3) the demand for node5 has a little probability decrease; (4) contract pressure is 1.9MPa; (5) When the customer end pressure is less than the contract pressure, the decision maker can reduce certain customer flows with lower priorities to ensure that the pressure is higher than the contract pressure.
Step 5: a safety margin is calculated based on the second dynamic event tree.
From fig. 2 it can be seen that node6 is at the end of the pipe, so that the pressure of node6 is the lowest in the overall system. All event sequences contained by node6 may be determined based on the second dynamic event tree: 1, the compressor is not in failure, the air source pressure is unchanged, and the user requirement is unchanged; 2, compressor failure-unchanged air source pressure-increased user demand; 3, the compressor is disabled, the air source pressure is unchanged, and the user requirement is unchanged; 4, the compressor is not disabled, the air source pressure is not reduced, and the user demand is increased; 5, the compressor is not disabled, the air source pressure is not reduced, the air source pressure is reduced, and the user demand is not increased; 6, the compressor is not disabled, the air source pressure is not reduced, the air source pressure is reduced, and the user demand is increased; 7, the compressor is not disabled, the air source pressure is reduced, the air source pressure is not reduced, and the user demand is not increased; 8, the compressor is not disabled, the air source pressure is reduced, the air source pressure is not reduced, and the user demand is increased; 9 compressor failure-air supply pressure reduction-user demand is not increased; 10 compressor failure-air supply pressure decrease-user demand increase.
The 10 event sequences do not include the invalid event sequence, and the pressure change data of node6 under the 10 different event sequences can be obtained through model simulation, wherein the pressure change data under the different event sequences are shown in fig. 7, and the abscissa is time, and the ordinate is pressure. It can be seen from fig. 7 that the fold line 5 is closest to the contract pressure in all the pressure change curves, so that the event sequence corresponding to the fold line 5 can be selected as the accident sequence for the safety margin reliability evaluation.
When the uncertainty factor in the running process of the system is considered, if all possible safety margins of the system are to be developed, millions of Monte Carlo samples are needed to be taken into model simulation to obtain the probability distribution of the safety margins. However, a great deal of mode cost is required, in order to reduce the calculation cost, the simulation times are limited to an acceptable range, and analysis simulation can be performed by using a sequence statistics method, so as to obtain a safety margin meeting a certain confidence coefficient, and the calculation mode is as follows:
(1) Sample determination, wherein a sample refers to the number of simulations per time. To ensure that the results obtained meet a certain confidence level (typically chosen to be 95%), the number of simulations per time needs to be determined according to the following equation:
Wherein ζ is confidence; n is the number of times of each simulation; gamma is a preset coverage value (generally 0.95 (0.05)), and generally the accuracy is required, and the higher the accuracy is, the larger the accuracy is selected; omega is the number of confidence intervals which are not in each simulation process; k is a parameter for the iterative process.
(2) And (5) single-time model simulation.
Under the preset variance condition, obtaining N input parameters x through Monte Carlo simulation sampling i (i=1, 2, …, N) and is brought into the model for simulation to obtain N outputs y i (i＝1,2,…,N)。
(3) The simulation was repeated.
Repeating the step (2) for G times (G is generally 50 and can be adjusted according to the situation), wherein the simulation result of G times is
(4) For each simulation (g=1, 2 …, G), a percentile estimate can be obtained for each time, i.e. the value of the gamma-th bit is obtained by order statistics (typically 5% is chosen, i.e. 5% x N-th bit), and the set of all percentiles can be expressed as
(5) Aggregating percentile estimatesThe median value (the number of median bits in all the order from large to small) is chosen as the estimated value to be taken into the safety margin calculation, the result having a 95% confidence level not exceeding the preset minimum value.
(6) The safety margin is calculated as follows:
wherein M (gamma, ζ) is a safety margin; Is a point estimate; l (L) j Is the contract pressure; y is j ref Is the reference pressure. Above y j ref For a preset y j An (alpha) reference value representing a characteristic safety parameter y of the system in a particular accident scenario j (alpha) and a preset allowable minimum value L j The difference between the two is used for determining whether the system state meets the safety requirement.
Taking m=1, ζ=γ=0.95, n=59, g=50, and taking the pressure of node6 as the reference pressure of the target event sequence, and taking the calculation method of the safety margin to calculate the safety margin of the comprehensive energy system under the condition of normal distribution of different variances.
The safety margin calculation results of the system under normal distribution of pressure variation with different variances are shown in table 2, and it can be seen that when the pressure variation variance is greater than 0.02, the safety margin of the system is less than zero, i.e., the system is considered unsafe.
TABLE 2 safety margin for different pressure profiles
Based on the same inventive concept, an energy system reliability determining device is also provided in the embodiments of the present application, as in the following embodiments. Because the principle of the energy system reliability determining device for solving the problem is similar to that of the energy system reliability determining method, the implementation of the energy system reliability determining device can refer to the implementation of the energy system reliability determining method, and the repetition is not repeated. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated. Fig. 8 is a block diagram of a configuration of an energy system reliability determining apparatus according to an embodiment of the present application, and as shown in fig. 8, may include: the configuration will be described below with reference to the acquisition module 801, the generation module 802, the first determination module 803, and the second determination module 804.
An obtaining module 801, configured to obtain supply and demand data of a target energy system;
a generating module 802, configured to generate a first dynamic event tree of the target energy system, where the first dynamic event tree includes all event sequences that may occur in the target energy system;
the first determining module 803 may be configured to determine, according to the supply and demand data and the first dynamic event tree, failure probability data corresponding to each of a plurality of system components of the target energy system within a preset time, where the plurality of system components include: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas;
the second determining module 804 may be configured to determine reliability of the target energy system under each system component condition according to the failure probability data.
The embodiment of the application further provides an electronic device, specifically, referring to a schematic diagram of an electronic device composition structure of the method for determining reliability of an energy system provided by the embodiment of the application shown in fig. 9, the electronic device may specifically include an input device 91, a processor 92, and a memory 93. The input device 91 may be used to input supply and demand data of the target energy system, in particular. The processor 92 may be specifically configured to obtain supply and demand data of the target energy system; generating a first dynamic event tree of the target energy system, wherein the first dynamic event tree comprises all event sequences possibly occurring in the target energy system; according to supply and demand data and a first dynamic event tree, determining failure probability data respectively corresponding to a target energy system under the condition of multiple system composition in preset time, wherein the multiple system composition conditions comprise: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas; and determining the reliability of the target energy system under the condition of each system composition according to the failure probability data. The memory 93 may be specifically configured to store parameters such as the first dynamic event tree, a plurality of system composition conditions, failure probability data, and reliability of the target energy system in each system composition condition, respectively.
In this embodiment, the input device may specifically be one of the main apparatuses for exchanging information between the user and the computer system. The input device may include a keyboard, mouse, camera, scanner, light pen, handwriting input board, voice input device, etc.; the input device is used to input raw data and a program for processing these numbers into the computer. The input device may also acquire and receive data transmitted from other modules, units, and devices. The processor may be implemented in any suitable manner. For example, the processor may take the form of, for example, a microprocessor or processor, and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), a programmable logic controller, and an embedded microcontroller, among others. The memory may in particular be a memory device for storing information in modern information technology. The memory may comprise a plurality of levels, and in a digital system, may be memory as long as binary data can be stored; in an integrated circuit, a circuit with a memory function without a physical form is also called a memory, such as a RAM, a FIFO, etc.; in the system, the storage device in physical form is also called a memory, such as a memory bank, a TF card, and the like.
In this embodiment, the specific functions and effects of the electronic device may be explained in comparison with other embodiments, which are not described herein.
There is further provided in an embodiment of the present application, a computer storage medium based on an energy system reliability determination method, where the computer storage medium stores computer program instructions, where the computer program instructions when executed may implement: acquiring supply and demand data of a target energy system; generating a first dynamic event tree of the target energy system, wherein the first dynamic event tree comprises all event sequences possibly occurring in the target energy system; determining failure probability data corresponding to the target energy system under the condition of a plurality of system components in preset time according to the supply and demand data and the first dynamic event tree, wherein the system components comprise: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas; and determining the reliability of the target energy system under the condition of each system component according to the failure probability data.
In the present embodiment, the storage medium includes, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read-Only Memory (ROM), a Cache (Cache), a Hard Disk (HDD), or a Memory Card (Memory Card). The memory may be used to store computer program instructions. The network communication unit may be an interface for performing network connection communication, which is set in accordance with a standard prescribed by a communication protocol.
In this embodiment, the functions and effects of the program instructions stored in the computer storage medium may be explained in comparison with other embodiments, and are not described herein.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the application described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than what is shown or described, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Although the present application provides method operational steps as described in the above embodiments or flowcharts, more or fewer operational steps may be included in the method, either on a routine basis or without inventive labor. In steps where there is logically no necessary causal relationship, the execution order of the steps is not limited to the execution order provided in the embodiments of the present application. The described methods, when performed in an actual apparatus or an end product, may be performed sequentially or in parallel (e.g., in a parallel processor or multithreaded environment) as shown in the embodiments or figures.
It is to be understood that the above description is intended to be illustrative, and not restrictive. Many embodiments and many applications other than the examples provided will be apparent to those of skill in the art upon reading the above description. The scope of the application should, therefore, be determined not with reference to the above description, but instead should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.
The foregoing description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and variations may be made to the embodiment of the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (8)
1. A method for determining reliability of an energy system, comprising:
acquiring supply and demand data of a target energy system;
generating a first dynamic event tree of the target energy system, wherein the first dynamic event tree comprises an event sequence which possibly occurs in the target energy system;
determining failure probability data corresponding to the target energy system under the condition of a plurality of system components in preset time according to the supply and demand data and the first dynamic event tree, wherein the system components comprise: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas;
Determining the reliability of the target energy system under the condition of each system component according to the failure probability data;
in the case that the target energy system contains electric conversion gas, after determining the reliability of the target energy system under the condition of each system composition according to the failure probability data, the method further comprises: acquiring target pressure of the target energy system; generating a second dynamic event tree of the target energy system by taking the target pressure as a process variable of the dynamic event tree; calculating a safety margin of the target energy system based on the second dynamic event tree; determining the reliability of an energy system containing electricity to gas according to the safety margin;
wherein calculating a safety margin of the target energy system based on the second dynamic event tree comprises: determining a plurality of event sequences contained in the second dynamic event tree; simulating based on the second dynamic event tree to obtain pressure change data corresponding to each event sequence in the plurality of event sequences; taking the pressure change data closest to the target pressure in the pressure change data corresponding to each event sequence as target pressure change data; taking the event sequence corresponding to the target pressure change data as a target event sequence; and calculating the safety margin of the target energy system under the target event sequence by utilizing Monte Carlo simulation sampling.
2. The method of claim 1, wherein the failure probability data comprises: the total failure probability of the target energy system in the preset time and the failure probability in each preset time period in the preset time.
3. The method of claim 1, wherein the plurality of system composition conditions comprises: the target energy system comprises liquefied natural gas but does not comprise electric conversion gas, the target energy system comprises electric conversion gas and liquefied natural gas, the target energy system does not comprise electric conversion gas and liquefied natural gas, and the target energy system comprises electric conversion gas but does not comprise liquefied natural gas.
4. The method of claim 1, wherein calculating a safety margin of the target energy system under the target event sequence using monte carlo analog sampling comprises:
determining a node with the lowest pressure in the target energy system;
taking the pressure at the node with the lowest pressure as a reference pressure;
and calculating the safety margin of the target energy system under the target event sequence according to the reference pressure by utilizing Monte Carlo simulation sampling.
5. The method of claim 1, wherein the sequence of target events comprises: the air supply pressure is reduced.
6. The method of claim 5, wherein calculating a safety margin of the target energy system under the target event sequence using monte carlo analog sampling comprises:
calculating corresponding variances of the reduction amplitudes of different gas source pressures;
respectively carrying out Monte Carlo simulation sampling under the condition of different variances to obtain sampling results under the condition of different variances;
and calculating the safety margin of the target energy system under the target event sequence under the condition of different variances according to the sampling result under the condition of the different variances.
7. An energy system reliability determination device, characterized by comprising:
the acquisition module is used for acquiring supply and demand data of the target energy system;
the generation module is used for generating a first dynamic event tree of the target energy system, wherein the first dynamic event tree comprises an event sequence which possibly occurs in the target energy system;
the first determining module is configured to determine failure probability data corresponding to each of the target energy systems under a plurality of system composition conditions within a preset time according to the supply and demand data and the first dynamic event tree, where the plurality of system composition conditions include: the target energy system comprises electric conversion gas, and the target energy system does not comprise electric conversion gas;
The second determining module is used for determining the reliability of the target energy system under the condition of each system composition according to the failure probability data;
in the case where the target energy system includes an electric power conversion gas, after determining the reliability of the target energy system in the respective system composition cases based on the failure probability data, the apparatus is further configured to: acquiring target pressure of the target energy system; generating a second dynamic event tree of the target energy system by taking the target pressure as a process variable of the dynamic event tree; calculating a safety margin of the target energy system based on the second dynamic event tree; determining the reliability of an energy system containing electricity to gas according to the safety margin;
wherein calculating a safety margin of the target energy system based on the second dynamic event tree comprises: determining a plurality of event sequences contained in the second dynamic event tree; simulating based on the second dynamic event tree to obtain pressure change data corresponding to each event sequence in the plurality of event sequences; taking the pressure change data closest to the target pressure in the pressure change data corresponding to each event sequence as target pressure change data; taking the event sequence corresponding to the target pressure change data as a target event sequence; and calculating the safety margin of the target energy system under the target event sequence by utilizing Monte Carlo simulation sampling.
8. An energy system reliability determining device comprising a processor and a memory for storing processor executable instructions which when executed by the processor implement the steps of the method of any one of claims 1 to 6.
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